retroharmonize: retroharmonize: Retrospective harmonization of survey data...
In antaldaniel/retroharmonize: Ex Post Survey Data Harmonization
retroharmonize R Documentation
retroharmonize: Retrospective harmonization of survey data files
Description
The goal of retroharmonize is to facilitate retrospective (ex-post)
harmonization of data, particularly survey data, in a reproducible manner.
The package provides tools for organizing the metadata, standardizing the
coding of variables, variable names and value labels, including missing
values, and for documenting all transformations, with the help of
comprehensive S3 classes.
import functions
Read data stored in formats with rich metadata, such as SPSS (.sav) files,
and make them usable in a programmatic context.
read_spss: read an SPSS file and record metadata for reproducibility
read_rds: read an rds file and record metadata for reproducibility
read_surveys: programmatically read a list of surveys
pull_survey: pull a single survey from a survey list.
subsetting functions
subset_surveys: remove variables from surveys that cannot be harmonized.
variable name harmonization functions
harmonize_survey_variables: Create a list of surveys with harmonized variable names.
codebook functions
codebook_create: A not yet working function.
codelist functions
codelist_create: A not yet working function.
variable label harmonization functions
Create consistent coding and labelling.
harmonize_values: Harmonize the label list across surveys.
harmonize_survey_values: Create a list of surveys with harmonized value labels.
na_range_to_values: Make the na_range attributes,
as imported from SPSS, consistent with the na_values attributes.
label_normalize removes special characters, whitespace,
and other typical typing errors and helps the uniformization of labels
and variable names.
survey harmonization functions
merge_surveys: Create a list of surveys with harmonized names and variable labels.
crosswalk_surveys: Create a list of surveys with harmonized variable names, harmonized
value labels and harmonize R classes.
crosswalk: Create a joined data frame of surveys with harmonized variable names, harmonized
value labels and harmonize R classes.
metadata functions
metadata_create: Createa metadata dataa from one or more survey.
metadata_survey_create: Create a joined metadata data frame from one survey.
create_codebook and codebook_waves_create
crosswalk_table_create: Create an initial crosswalk table from a metadata data frame.
documentation functions
Make the workflow reproducible by recording the harmonization process.
document_survey_item: Returns a list of the current and historic coding,
labelling of the valid range and missing values or range, the history of the variable names
and the history of the survey IDs.
document_surveys: Document the key attributes surveys in a survey list.
type conversion functions
Consistently treat labels and SPSS-style user-defined missing
values in the R language.
survey helps constructing a valid survey data frame, and
labelled_spss_survey helps creating a vector for a
questionnaire item.
as_numeric: convert to numeric values.
as_factor: convert to labels to factor levels.
as_character: convert to labels to characters.
as_labelled_spss_survey: convert labelled and labelled_spss
vectors to labelled_spss_survey vectors.
Author(s)
Maintainer: Daniel Antal daniel.antal@dataobservatory.eu (ORCID)
Other contributors:
Marta Kolczynska mkolczynska@gmail.com (ORCID) [contributor]
See Also
Useful links:
antaldaniel/retroharmonize documentation built on Dec. 31, 2024, 9:52 p.m.
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| retroharmonize | R Documentation |
retroharmonize: Retrospective harmonization of survey data files
Description
The goal of retroharmonize is to facilitate retrospective (ex-post)
harmonization of data, particularly survey data, in a reproducible manner.
The package provides tools for organizing the metadata, standardizing the
coding of variables, variable names and value labels, including missing
values, and for documenting all transformations, with the help of
comprehensive S3 classes.
import functions
Read data stored in formats with rich metadata, such as SPSS (.sav) files,
and make them usable in a programmatic context.
read_spss: read an SPSS file and record metadata for reproducibility
read_rds: read an rds file and record metadata for reproducibility
read_surveys: programmatically read a list of surveys
pull_survey: pull a single survey from a survey list.
subsetting functions
subset_surveys: remove variables from surveys that cannot be harmonized.
variable name harmonization functions
harmonize_survey_variables: Create a list of surveys with harmonized variable names.
codebook functions
codebook_create: A not yet working function.
codelist functions
codelist_create: A not yet working function.
variable label harmonization functions
Create consistent coding and labelling.
harmonize_values: Harmonize the label list across surveys.
harmonize_survey_values: Create a list of surveys with harmonized value labels.
na_range_to_values: Make the na_range attributes,
as imported from SPSS, consistent with the na_values attributes.
label_normalize removes special characters, whitespace,
and other typical typing errors and helps the uniformization of labels
and variable names.
survey harmonization functions
merge_surveys: Create a list of surveys with harmonized names and variable labels.
crosswalk_surveys: Create a list of surveys with harmonized variable names, harmonized
value labels and harmonize R classes.
crosswalk: Create a joined data frame of surveys with harmonized variable names, harmonized
value labels and harmonize R classes.
metadata functions
metadata_create: Createa metadata dataa from one or more survey.
metadata_survey_create: Create a joined metadata data frame from one survey.
create_codebook and codebook_waves_create
crosswalk_table_create: Create an initial crosswalk table from a metadata data frame.
documentation functions
Make the workflow reproducible by recording the harmonization process.
document_survey_item: Returns a list of the current and historic coding,
labelling of the valid range and missing values or range, the history of the variable names
and the history of the survey IDs.
document_surveys: Document the key attributes surveys in a survey list.
type conversion functions
Consistently treat labels and SPSS-style user-defined missing
values in the R language.
survey helps constructing a valid survey data frame, and
labelled_spss_survey helps creating a vector for a
questionnaire item.
as_numeric: convert to numeric values.
as_factor: convert to labels to factor levels.
as_character: convert to labels to characters.
as_labelled_spss_survey: convert labelled and labelled_spss
vectors to labelled_spss_survey vectors.
Author(s)
Maintainer: Daniel Antal daniel.antal@dataobservatory.eu (ORCID)
Other contributors:
Marta Kolczynska mkolczynska@gmail.com (ORCID) [contributor]
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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